Extant methods of risk detection rely on rule-based determinations, decision trees, or artificial neural networks. However, each extant method suffers from shortfalls. For example, rule-based determinations tend to have high error rates, which are inconvenient for financial markets. Although decision trees may have lower error rates than rule-based determinations, they often rely on manual verification along one or more branches, as well as increasing complexity and processing resources. Finally, artificial neural networks fail to recognize many patterns and exponentially increase resource drain as the number of hidden neuron layers increases linearly.
Moreover, extant rules, trees, and neural networks must be implemented separately. This both reduces processing efficiency and requires further resource drain and algorithmic complexity to handle disparate results from the rules, trees, and networks.
Additionally inefficiencies inhere in the storage of discrete data representing transactions. This discrete data is generally stored in a relational database or a graph database. The former relies on resource-costly searches and concatenations while the latter relies on exponentially costly nodal traversals.